LLMs as a “Virtual Security Consultant”: Supporting the Generation of Security Requirements, Evil User Stories, and Test Cases for Teams with Low Security Proficiency
Security Engineering; Software Security; Threat Modeling; LLM; STRIDE; Automation; Security Tests; Agile; DevSecOps.
This work investigates the use of Large Language Models (LLMs) as security assistants to
support software development teams with loy security proficiency throughout the software
development lifecycle. It proposes an artifact composed of microservices that assist in the
identification and structurinf of threats, as well as in the elicitation of security requirements,
business rules, and user stories provided by the developers or QA members. The approach
is based on Gary McGraw’s Touchpoints, focusing specifically on automating the Security
Requirements and Architectural Risk Analysis points, using the STRIDE methodology as
an analysis engine to identify threats. It uses a structured process that integrates LLMs
as a mechanism to support reasoning and decision-making, and not as a substitute for
human experts. The goal is to reduce cognitive effort, support security reasoning, and
improve security awareness and maturity in teams with limited expertise. The research
follows the Design Science Research methodology, encompassing the design, development,
demonstration, and evaluation of the artifact through a controlled study conducted in
the context of systems maintained by STI/UFRN. The expected results aim to assess to
what extent an LLM-based artifact improves quantity, diversity, and quality of identified
threats and security requirements when compared to predominantly manual approaches,
positioning LLM as “virtual security consultant” for low-proficiency teams